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ORIGINAL RESEARCH ARTICLE
published: 12 March 2013
doi: 10.3389/fnhum.2013.00072
The effects of physical activity on functional MRI activation
associated with cognitive control in children: a randomized
controlled intervention
Laura Chaddock-Heyman1
*, Kirk I. Erickson 2,Michelle W. Voss3,AnyaM.Knecht
1,
Matthew B. Pontifex 4, Darla M. Castelli5,Charles H. Hillman 6and Arthur F. Kramer 1
1Department of Psychology, The Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
2Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
3Department of Psychology, The University of Iowa, Iowa City, IA, USA
4Department of Kinesiology, Michigan State University, East Lansing, MI, USA
5Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA
6Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Edited by:
Burkhard Pleger, Max Planck
Institute for Human Cognitive and
Brain Sciences, Germany
Reviewed by:
Marco Taubert, Max-Planck-Institute
for Human Cognitive and Brain
Sciences, Germany
Hubert R. Dinse, Ruhr-Universität
Bochum, Germany
*Correspondence:
Laura Chaddock-Heyman,
Department of Psychology, The
Beckman Institute for Advanced
Science and Technology, University
of Illinois at Urbana-Champaign,
405 North Mathews Avenue,
Urbana, IL 61801, USA.
e-mail: lchaddo2@illinois.edu
This study used functional magnetic resonance imaging (fMRI) to examine the influence of
a 9-month physical activity program on task-evoked brain activation during childhood. The
results demonstrated that 8- to 9-year-old children who participated in 60+min of physical
activity, 5 days per week, for 9 months, showed decreases in fMRI brain activation in the
right anterior prefrontal cortex coupled with within-group improvements in performance on
a task of attentional and interference control. Children assigned to a wait-list control group
did not show changes in brain function. Furthermore, at post-test, children in the physical
activity group showed similar anterior frontal brain patterns and incongruent accuracy
rates to a group of college-aged young adults. Children in the wait-list control group still
differed from the young adults in terms of anterior prefrontal activation and performance
at post-test. There were no significant changes in fMRI activation in the anterior cingulate
cortex (ACC) for either group. These results suggest that physical activity during childhood
may enhance specific elements of prefrontal cortex function involved in cognitive control.
Keywords: activation, brain, children, fitness, fMRI, physical activity
INTRODUCTION
Physical activity and higher aerobic fitness are associated with
improved brain function across the lifespan (Kramer et al., 1999;
Hillman et al., 2005, 2009; Davis et al., 2011; Kamijo et al., 2011;
Pontifex et al., 2011; Voss et al., 2011; Chaddock et al., 2012a).
Prior studies have reported that substantial effects of physical
activity occur on tasks that measure cognitive control (Hawkins
et al., 1992; Colcombe and Kramer, 2003; Tomporowski et al.,
2008), which refers to aspects of cognition that describe the abil-
ity to flexibly adapt behavior toward specific goals, maintenance
of these goals, monitoring of errors, and formulation of decisions
(Botvinick et al., 2001; Braver and Barch, 2006). To achieve high
levels of cognitive control, individuals must be able to selectively
attend to relevant information, filter distractions, and inhibit
inappropriate response tendencies (Bunge and Crone, 2009). Of
particular interest to this investigation, higher fit and physically
active children have been found to outperform their lower fit
peers on tasks of cognitive control [e.g., flanker tasks (Hillman
et al., 2009; Pontifex et al., 2011; Voss et al., 2011; Chaddock
et al., 2012a), Stroop tasks (Buck et al., 2008), n-back tasks
(Kamijo et al., 2011), and real world street crossing multitasking
paradigms (Chaddock et al., 2012b)].
Here, functional magnetic resonance imaging (fMRI) was
used to examine the influence of a 9-month physical activity
program on brain activation patterns associated with cognitive
control during childhood. Only a few studies with children have
used fMRI to examine how physical activity and aerobic fitness
relate to brain function during tasks engaging cognitive control
(Chaddock et al., 2011; Davis et al., 2011; Voss et al., 2011). In one
study by Davis et al. (2011), overweight children (age 7–13 years)
involved in 13 weeks of aerobic games showed improvements in
cognitive control (i.e., a “planning” score said to measure strat-
egy and self-regulation) and increases in frontal fMRI activation
during an antisaccade task, which provides a measure of response
inhibition. However, the interpretation of the fMRI results was
constrained because performance on the antisaccade task was not
reported. Whereas Davis et al. (2011) suggested that increased
frontal activation with physical activity may be associated with
improvements in cognitive control, two other studies showed
that decreased frontal activation in higher fit children was asso-
ciated with better cognitive control (Voss et al., 2011; Chaddock
et al., 2012a). In a study by Chaddock et al. (2012a), children
with higher aerobic fitness levels showed reduced activation in
the frontal cortex from early to late stages of a flanker task, cou-
pled with maintenance of attentional and interference control.
It is noteworthy that these fitness differences in activation were
only apparent for incongruent flanker trials that required sub-
stantial cognitive control. During congruent trials, both higher fit
Frontiers in Human Neuroscience www.frontiersin.org March2013|Volume7|Article72|1
HUMAN NEUROSCIENCE
Chaddock-Heyman et al. Physical activity and fMRI activation
and lower fit children showed decreases in activation and main-
tenance of task performance. In conjunction with these findings,
Voss e t al. (2011) showed that higher fit children exhibited less
activation than lower fit children in a network of brain regions
including anterior frontal areas involved in task maintenance
and cognitive control, coupled with higher accuracy rates during
incongruent flanker task trials that required increased cognitive
control. Together, previous studies suggest that physical activity
and aerobic fitness influence brain function in regions such as the
frontal cortex as well as the ability to adapt neural processes to
meet and maintain task goals (Davis et al., 2011; Voss et al., 2011;
Chaddock et al., 2012a).
The present study examined brain function, in terms of acti-
vation and task performance, during a task of cognitive control
in children participating in an after school physical activity inter-
vention compared to children in a wait-list control group. Such a
longitudinal design significantly strengthens and extends correla-
tional research on aerobic fitness and childhood brain function
(Voss et al., 2011; Chaddock et al., 2012a). Although higher
fit and lower fit child groups in previous cross-sectional stud-
ies did not differ in variables known to influence cognitive and
brain health [e.g., IQ, age, socioeconomic status (SES), puber-
tal timing, Attention Deficit Hyperactivity Disorder (ADHD)],
cross-sectional designs raise the possibility that the observed dif-
ferences were caused by other unmeasured factors (e.g., genes,
personality characteristics, nutrition, intellectual stimulation,
etc.). Randomized, controlled trials like the present study are
necessary to account for potential selection bias and to establish
direct and causal associations among physical activity, aerobic fit-
ness and brain activation patterns in children. Here, children were
randomly assigned to either an after school physical activity group
or wait-list control group in order to examine how participation
in a physical activity program aimed at improving aerobic fitness
influences performance on a task of cognitive control and brain
function associated with cognitive control.
In addition, in the present study, to further strengthen and
extend previous research, the brain function of the physical
activity intervention children and wait-list control children was
compared to the activation of college-aged young adults. Adult
task performance and activation patterns are often characterized
as the “mature” or “optimal” model of brain function to which
children can be compared (Luna et al., 2010). Although fMRI
studies of age-related differences in cognitive control report a
variety of results (see Luna et al., 2010 for a review), the majority
of studies demonstrate increased frontal activity in children rela-
tive to adults (e.g., Casey et al., 1997; Durston et al., 2002; Booth
et al., 2003; Scherf et al., 2006; Velanova et al., 2008), coupled
with poorer task performance during cognitive tasks of inhibition
(e.g., Go/NoGo, flanker, antisaccade tasks; Diamond, 2006)and
working memory (e.g., n-back, visual spatial working memory;
Baddeley, 1986; Bunge and Crone, 2009). Nevertheless, through-
out childhood, there are continued improvements in cognitive
control, and the frontal cortex plays a primary role in perfor-
mance changes (Luna et al., 2010). Thus, this study explored
how participation in physical activity during childhood influences
brain function in frontal brain regions, as well as how the changes
mirror patterns of adult activation and cognitive abilities.
The first goal of this study was to determine the brain areas,
especially frontal brain regions, in children that were associated
with an fMRI task of cognitive control. For example, cognitive
control has been associated with frontal regions including (1) the
anterior prefrontal cortex, hypothesized to maintain task goals,
(2) the lateral prefrontal cortex, hypothesized to initiate flexible
adjustments in cognitive control, and play a role in working mem-
ory, and (3) the anterior cingulate cortex (ACC), hypothesized
to evaluate and monitor conflict and thereby signal the need to
adjust control (Hazeltine et al.,2000; Botvinick et al., 2001; Bunge
et al., 2002; Braver and Barch, 2006; Dosenbach et al., 2007). The
second goal was to examine whether brain function in the regions
associated with the task changed from pre-test to post-test in
the physical activity intervention group compared to any changes
occurring in the wait-list control group. It was hypothesized that
children involved in a 9-month physical activity program would
show improvements in performance on the task, coupled with
decreased activation in frontal brain regions from pre-test to post-
test, relative to a wait-list control group. The third goal was to
compare the activation patterns of both groups of children to
the activation of young adults. It was predicted that the frontal
activation patterns and performance of physically active children
at post-test would show greater similarity to the brain function
of young adults, relative to the post-test patterns of the wait-list
control children.
METHODS
PARTICIPANTS
Eight- to nine-year-old children were recruited from the Urbana,
Illinois School District 116. All children completed demographic
assessments, a VO2max test to assess aerobic fitness, and an MRI
session (which included a structural and functional MRI scan) at
pre-test (i.e., before randomization into a physical activity inter-
vention group or a wait-list control group) and post-test (i.e.,
after the completion of the intervention, approximately 9 months
later).
Thirty-two children were eligible for the study. Seven children
(three physical activity intervention children fourwait-list control
children) were excluded from the analyses for excessive motion
during the fMRI task. Two children were excluded from the anal-
yses (two physical activity intervention children) for less than
chance task performance. Accordingly, 23 children, with pre-test
and post-test fMRI data, were included in the final analyses, with
14 children (seven female, seven male) assigned to the physical
activity intervention group and nine children (six female, three
male) assigned to the wait-list control group. Twenty-four young
adults (10 female, 14 male) (mean age of 22.5 years) were also
recruited from the University of Illinois to compare children’s
brain and performance patterns to a young adult group.
DEMOGRAPHIC ASSESSMENTS AND FITNESS TESTING
To be eligible for the study, children had to have a Kaufman
Brief Intelligence Test (KBIT) composite score greater than 85
(Kaufman and Kaufman, 1990)andqualifyasprepubescent
(Tanner puberty score ≤2; Taylor et al., 2001). Children were
also screened for the presence of attentional disorders using the
ADHD Rating Scale IV (DuPaul et al., 1998), and were excluded
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Chaddock-Heyman et al. Physical activity and fMRI activation
if they scored above the 85th percentile. Body mass index (BMI)
wascalculatedasweight(kg)/height(cm)
2,andSESwasdeter-
mined by creating a trichotomous index: participation in a free or
reduced-price meal program at school, the highest level of educa-
tion obtained by the child’s mother and father, and the number
of parents who worked full-time (Birnbaum et al., 2002; Hillman
et al., 2012).
Eligible children were further required to (1) report an absence
of school-related learning disabilities (i.e., individual education
plan related to learning), adverse health conditions, physical
incapacities, or neurological disorders, (2) report no use of
medications that influence central nervous system function, (3)
demonstrate right handedness (as measured by the Edinburgh
Handedness Questionnaire; Oldfield, 1971), (4) complete a mock
MRI session successfully to screen for claustrophobia in an
MRI machine, (5) be capable of performing physical activity,
and (6) sign an informed assent approved by the University of
Illinois at Urbana-Champaign. A legal guardian also provided
written informed consent in accordance with the Institutional
Review Board of the University of Illinois at Urbana-Champaign.
Children were paid for their time ($10/h for demographic assess-
ments and fitness testing and $15/h for MRI testing).
AEROBIC FITNESS TESTING
Children completed a VO2max test at pre-test and post-test
to assess aerobic fitness. The aerobic fitness of each child was
measured as maximal oxygen consumption (VO2max) during
a graded exercise test (GXT). The GXT employed a modified
Balke Protocol and was administered on a LifeFitness 92T motor-
driven treadmill (LifeFitness, Schiller Park, IL). Children walked
and/or ran on a treadmill at a constant speed with increasing
grade increments of 2.5% e very 2 min until volitional exhaustion
occurred.
Oxygen consumption was measured using a computerized
indirect calorimetry system (ParvoMedics True Max 2400) with
averages for VO2and respiratory exchange ratio (RER) assessed
every 20 s. A polar heart rate (HR) monitor (Polar WearLink+
31; Polar Electro, Finland) was used to measure HR throughout
the test, and ratings of perceived exertion (RPE) were assessed
every 2 min using the children’s OMNI scale (Utter et al., 2002).
Maximal oxygen consumption was expressed in ml/Kg/min and
VO2max was based upon maximal effort as evidenced by (1)
a plateau in oxygen consumption corresponding to an increase
of less than 2 ml/Kg/min despite an increase in workload; (2) a
peak HR ≥185 beats per minute (American College of Sports
Medicine, 2006) and an HR plateau (Freedson and Goodman,
1993); (3) RER ≥1.0 (Bar-Or, 1983); and/or (4) a score on the
children’s OMNI RPE scale ≥8(Utter et al., 2002).
PHYSICAL ACTIVITY TRAINING INTERVENTION AND WAIT-LIST
CONTROL GROUP
The physical activity intervention occurred for 2 h after
each school day, from September until May, for 150 days
out of the 170-day school year. The program, Fitness
Improves Thinking in Kids (FIT Kids) (http://clinicaltrials.
gov/ct2/show/NCT01334359?term=FITKids&rank=1)isbased
on the Child and Adolescent Trial for Cardiovascular Health
(CATCH) curriculum (McKenzie et al., 1994). CATCH is one of
the only evidenced-based physical activity programs to incor-
porate educational, behavioral, and environmental components
(Luepker et al., 1996; Nader et al., 1999), resulting in moderate
to vigorous physical activity engagement. Although the primary
aim of the program targeted improving aerobic fitness through
engagement in a variety of age-appropriate physical activities,
it was also designed to meet a child’s daily need for physical
activity by providing 3 or more days per week of aerobic activity
as well as muscle and bone strengthening activities (United
States Department of Health and Human Services, 2008). The
environment was non-competitive and integrated activities such
as fitness activities and low organized games (Castelli et al., 2011).
Within a daily lesson, the children participated in an average
of 76.8 min of moderate to vigorous physical activity (recorded
by E600 Polar HR monitors; Polar Electro, Finland, and Accusplit
Eagle 170 pedometers, San Jose CA), thus exceeding the national
physical activity guideline of 60 min of moderate to vigorous
physical activity per day (United States Department of Health
and Human Services, 2008). Children completed stations that
focused on a specific health-related fitness component (e.g., car-
diorespiratory endurance, muscular strength, motor skills) and
participated in game play. The activities were aerobically demand-
ing, but simultaneously provided opportunities to refine motor
skills. The program also included consumption of a healthy snack
and the introduction of a themed educational component related
to health promotion (i.e., goal setting, self-management). On the
weekends, the children were encouraged to continue their partic-
ipation in physical activity with their family, and physical activity
worksheets were utilized during school holidays to log continued
engagement. Average attendance across the 9-month intervention
was 82% (SD =13.3%).
The wait-list control group was not contacted following ran-
domization. They completed all facets of the pre-test and post-
test, similar to those children who were randomized into the
after school physical activity intervention. As incentive to stay in
the study, children in the wait-list control group were afforded
the opportunity to participate in the intervention during the
following school year.
IMAGING METHOD
Children and young adults completed structural and functional
MRI scans. Prior to scanning, all participants were tested for
visual acuity, and corrective lenses were added to MRI safe plastic
frames to ensure a corrected vision of at least 20/40 while in the
scanner. The lenses and frames did not obstruct a mirror above
participants’ eyes that enabled them to view images on a back
projection.
Structural MRI protocol
High resolution T1-weighted brain images were acquired using
a 3D MPRAGE (Magnetization Prepared Rapid Gradient Echo
Imaging) protocol with 192 contiguous axial slices, collected in
ascending fashion parallel to the anterior and posterior com-
missures, echo time (TE) =2.32 ms, repetition time (TR) =
1900 ms, field of view (FOV) =230 mm, acquisition matrix
256 ×256 mm, slice thickness =0.90 mm, and flip angle =9◦.
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Chaddock-Heyman et al. Physical activity and fMRI activation
All images were collected on a Siemens Magnetom Trio 3T
whole-body MRI scanner.
Functional MRI protocol
Functional MRI scans were acquired during an event-related cog-
nitive control task. Five shapes were presented, and participants
were instructed to look at the middle shape. Three task conditions
were included: neutral (-->--,--<--), incongruent (<<><<,
>><>>), and NoGo (<<X<<,>>X>>)trials(seeFigure 1).
When the middle arrow pointed to the left, participants were
instructed to press a button with their left index finger. When the
middle arrow pointed to the right, participants were instructed
to press a button with their right index finger. When the mid-
dle shape was an X, participants were told not to press a button.
Participants were asked to respond as quickly and accurately
as possible. The neutral condition was designed to require less
attentional, interference and inhibitory control. The incongruent
condition required attentional and interference control to filter
potentially misleading flankers that were mapped to incorrect
behavioral responses. The NoGo condition required subjects to
inhibit a prepotent tendency to respond, given that the majority of
trials (i.e., incongruent, neutral) required an active “go” response.
During the task, 40 trials of each of the three possible con-
ditions (-->--,--<--,>><>>,<<><<,>>X>>,<<X<<)
were presented in a random order. The response window included
thepresentationofthearrayofshapesfor500ms,followed
by a blank screen for 1000 ms. Each stimulus array was sepa-
rated by a fixation cross (+) presented for 1500 ms. Forty addi-
tional fixation crosses that jittered between 1500 and 6000 ms
were also randomly presented after the constant 1500ms fixa-
tion cross throughout the task. The jitter prevented participants
from expecting a specific frequency of responding. White shapes
and white fixation crosses were presented on a black back-
ground. The participant was engaged in the task for about 6 min.
Stimulus presentation, timing, and task performance measures
were controlled by E-Prime software (Psychology Software Tools,
Sharpsburg, Pennsylvania).
For the fMRI protocol during the flanker task, a fast echo-
planar imaging (EPI) sequence with Blood Oxygenation Level
Dependent (BOLD) contrast was employed. A total of 328
FIGURE 1 | Sample stimuli for the fMRI task of cognitive control.
volumes (TR =1500 ms; TE =25 ms; flip angle =80◦)were
collected for each participant.
Image analysis
Neuroimaging data analysis was conducted using FSL 4.1.9
(FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). All child
data and young adult data followed the same pre-processing,
registration, and first level analysis stream. Preprocessing of the
functional data included motion correction via a rigid body algo-
rithm in MCFLIRT (Jenkinson et al., 2002), removal of non-brain
structures using BET (Brain Extraction Technique; Smith et al.,
2002), spatial smoothing using a 5.0 mm FWHM (full width at
half maximum) three-dimensional Gaussian kernel, and tempo-
ral filtering with a high pass frequency cut-off of 40s. In addition,
the high-resolution T1 structural images of each participant were
skull stripped using BET (Smith et al., 2002). The functional
images of each participant were spatially registered to his/her
individual skull-stripped high-resolution anatomical image, and
then to an MNI template in stereotaxic space. Registrations were
conducted using a 12-parameter affine transformation [FMRIB’s
Linear Image Registration Tool (FLIRT); (Jenkinson and Smith,
2001; Jenkinson et al., 2002)].
Regression-based analysis of each participant’s fMRI data was
carried out using FSL’s FEAT Version 5.98 (Beckmann et al.,
2003). The time series at each voxel was modeled against the
expected time series model derived by convolving the onset of
each event type (incongruent, neutral, NoGo) with a double-
gamma function, representing the expected time course of the
hemodynamic response function. Only correct task trials were
included in the model, and error trials were entered as covariates
of no interest. The same high pass temporal filtering applied to
the data was applied to the general linear model for the best possi-
ble match between the data and model. In addition, the temporal
derivative was entered into the model (i.e., shifting the waveform
slightly in time) to achieve a better model fit to the data and
to reduce unexplained noise. The first level analysis calculated a
parameter estimate for the fMRI model at each voxel to estimate
how strongly the model waveform fits the data, and this analy-
sis resulted in voxel-wise statistical parametric maps for the entire
brain of each participant for each task condition.
Next, the brain maps of all children at pre-test and post-test
were forwarded to a higher-level mixed-effects group analysis to
localize areas of cortex in all child participants at pre-test and
post-test that were activated during the task of cognitive con-
trol. Higher-level group analyses were carried out using FLAME
(FMRIB’s Local Analysis of Mixed Effects). To ensure that individ-
ual and group differences in gray matter volume did not confound
the results, estimated total mean gray matter volume for each
child at pre-test and post-test, smoothed with 3 mm HWHM ker-
nel, was used as a voxel-wise covariate in the higher level FLAME
analyses.
A Z statistic map that showed average activation during all task
conditions relative to fixation baseline was created for all children,
across pre-test and post-test. This conjunction map was used
to locate and extract ROIs so that the regions would be chosen
independently of effects associated with group (physical activ-
ity intervention, wait-list control), task condition (incongruent,
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Chaddock-Heyman et al. Physical activity and fMRI activation
neutral, NoGo), and time (pre-test, post-test). This technique
helped ensure that the localization of the ROIs was unbiased in
relation to the predictor variables.
Because of the widespread activation for the task, a more con-
servative statistical threshold to identify clusters for the regions-
of-interest analysis was employed (Kriegeskorte et al., 2009).
Accordingly, the Zstatistic maps were thresholded at Z>6.00,
with a (corrected) cluster significance threshold of p<0.05
(Worsley, 2001). Of particular interest, two clusters in the frontal
cortex were observed: (1) the right anterior prefrontal cortex
(right frontal pole) (with x,y,zvoxel coordinates of 27, 94, 40,
Z=6.2) (see Figure 2), and (2) the ACC (with x,y,zvoxel coor-
dinates of 44, 69, 55, Z=7.1) (see Figure 2). Eight millimeter
(diameter) masks (which contained 125 voxels, 1000 mm3)were
created around each peak to use as functionally defined ROIs
FIGURE 2 | The 8 mm (diameter) box ROIs (1000 mm3) in the frontal
cortex, derived from an average activation map during incongruent,
neutral and NoGo conditions of the task of cognitive control,
across both physical activity and control child groups at pre-test
and post-test (thresholded at Z>6). Right anterior prefrontal
cortex =yellow; ACC =red; average activation map =blue.
(see Figure 2). Mean percent signal change (versus fixation) was
extracted for incongruent, neutral, and NoGo conditions. Note
that some activation was also seen in the insula and occipital lobe,
but these areas are not a focus of the paper given lack of effects and
lack of hypotheses in these areas.
STATISTICAL ANALYSIS
Multivariate repeated measures ANOVAs were first conducted to
explore changes in aerobic fitness (VO2max) and task perfor-
mance in the physical activity and wait-list control groups from
pre-test to post-test. Given apriorihypotheses, paired t-tests were
also conducted to compare within-group changes in fitness and
task performance. In addition, task performance of the physical
activity intervention group and wait-list control group at pre-test
and post-test were compared to the young adult group.
Next, the peaks of activation in the brain during the fMRI
task of cognitive control, in all children, across task conditions,
at pre-test and post-test were determined. Within these regions
of interest (ROIs), repeated measures 2 (group: physical activity
intervention, wait-list control) ×3 (task condition: incongru-
ent, neutral, NoGo) ×2 (time: pre-test, post-test) ANOVAs were
conducted to explore changes in mean percent signal change in
the ROIs. If the omnibus ANOVA reached significance, post-hoc
comparisons were performed (with Bonferroni-corrected t-tests)
to examine how activation patterns within each task condition
changed with participation in physical activity or assignment
to a wait-list control group. Further, independent t-tests were
conducted between the physical activity intervention group, wait-
list control group, and young adults at pre-test and post-test to
explorehowchangesinactivationovertimeinchildrencompared
to activation in a young adult sample. The family-wise alpha level
was set at p=0.05.
RESULTS
PARTICIPANT DEMOGRAPHI CS AND AEROBIC FITNESS
Demographic and fitness data at pre-test and post-test are pro-
vided in Tab l e 1 . Demographic and fitness variables of age, gen-
der, race, KBIT (IQ), SES, pubertal timing, and VO2max, did
not differ between the physical activity intervention group and
the wait-list control group (all p>0.05).
The physical activity intervention group showed a 6% increase
in VO2max percentile from pre-test to post-test [t(13)=2.0,
p=0.06], and the wait-list control group showed a 2% increase
in VO2max percentile [t(8)=1.0, p=0.3]. There was also a
marginal effect of time for VO2max percentile [F(1,21)=4.1,
p=0.057], but no group ×time interaction [F(1,29)=0.9, p=
0.3]. Together, the data suggest an increase in VO2max in all
children with age and development (Janz and Mahoney, 1997).
However, the physical activity intervention group showed addi-
tional within-group gains in VO2max as a function of their daily
exposure to physical activity.
TASK PERFORMANCE
All task performance data for the physical activity intervention
group, the wait-list control group, and the young adults are in
Ta b l e 2 . To confirm the efficacy of the task, performance dif-
ferences in all children during the three task conditions were
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Chaddock-Heyman et al. Physical activity and fMRI activation
Table 1 | Mean (SD) for physical activity and control groups at pre-test and post-test.
Physical activity Control
Pre-test Post-test Pre-test Post-test
Age (years) 8.9 (0.7) 9.6 (0.7) 8.9 (0.4) 9.5 (0.5)
Gender 7 girls, 7 boys 6 girls, 3 boys
IQ 122.3 (14.9) 122.6 (11.8) 114.4 (16.2) 116.4 (18.7)
Pubertal timing 1.3 (0.4) 1.5 (0.4) 1.1 (0.2) 1.5 (0.8)
SES 2.2 (0.9) 2.3 (0.9) 1.8 (0.9) 1.7 (0.9)
VO2max (ml/Kg/min) 38.3 (4.0) 40.6 (4.1) 37.3 (6.2) 39.0 (4.5)
VO2max percentile 14.0 (14.9)a20.0 (18.6)a14.8 (12.5) 16.9 (13.5)
BMI (kg/cm2) 18.4 (3.6) 18.7 (4.4) 19.3 (3.7) 20.0 (3.4)
Note: IQ, composite standardized score of intelligence quotient from the Kaufman Brief Intelligence Test (Kaufman and Kaufman, 1990), SES, socioeconomic status.
Values that share a common superscript are significantly different at p <0.05.
Table 2 | Mean task performance (SD) (range) for the physical activity (PA) and control (C) groups at pre-test and post-test, as well as the
young adult group.
PA pre-test PA post-test C pre-test C post-test Young
Incongruent RT (ms) 919.4 (177.3)
(658.3–1246.7)a**
801.5 (173.7)
(602.2–1163.0)a**
937.3 (108.6)
(793.4–1166.7)
841.8 (132.6)
(663.6–1141.3)
606.1 (86.9)
(464.6–760.1)
Neutral RT (ms) 826.6 (139.6)
(652.3–1072.6)a**
755.6 (157.7)
(585.8–1108.3)a**
850.1 (84.1)
(693.7–975.4)
789.6 (132.3)
(649.6–1098.5)
551.8 (83.1)
(413.5–708.7)
Incongruent accuracy
(% correct)
85.9 (9.8)
(70.0–98.0)a*
91.2 (4.9) (85–100)a*83.9 (14.8)
(60.0–100.0)
86.9 (9.9) (70.0–98.0) 92.6 (4.6) (78.0–100)
Neutral accuracy
(% correct)
91.6 (6.2)
(78.0–100)a**
95.7 (3.5)
(88.0–100)a**
89.2 (12.6) (63.0–100) 91.1 (5.7) (83.0–100) 96.5 (1.9) (93.0–100)
NoGo accuracy
(% correct)
98.8 (1.6) (95.0–100) 98.9 (2.1) (93.0–100) 98.6 (1.8) (95.0–100) 98.6 (1.8) (95.0–100) 98.9 (2.2) (93.0–100)
Note: Values that share a common superscript are significantly different at ∗∗p<0.05. ∗p<0.1. Effect sizes (Cohen’s d): PA pre-test to post-test changes: incongru-
ent RT-ES =0.67, neutral RT-ES =0.48, incongruent accuracy-ES =0.68, neutral accuracy-ES =0.81, NoGo accuracy-ES =0.05. C pre-test to post-test changes:
incongruent RT-ES =0.78, neutral RT-ES =0.54, incongruent accuracy-ES =0.23, neutral accuracy-ES =0.19, NoGo accuracy-ES =0.
explored. In general, the performance data suggested that the
incongruent flanker task provided the greatest challenge to the
participants’ ability to pay attention, suppress distraction, and
maintain a task set. That is, shorter reaction time (RT) for neutral
trials (M=805.5 ms, SD =130.4 ms) compared to incongruent
trials (M=875.0 ms, SD =152.0 ms) was found at both pre-
test and post-test [main effect of task condition, F(1,21)=44.1,
p<0.001]. Higher accuracy for neutral trials (M=94.7%, SD =
0.05) compared to incongruent trials (M=90.3%, SD =0.07%)
was also found at both pre-test and post-test [t(22)=6.1, p<
0.001] [main effect of task condition, F(2,21)=42.2, p<0.001].
However, inconsistent with predictions, children performed at
near ceiling accuracy rates during the NoGo task condition. NoGo
accuracy (M=98%, SD =1.5%) was significantly higher than
incongruent accuracy (M=87%, SE =1.6%) [t(22)=7.3, p<
0.001] and neutral accuracy (M=92%, SD =7.7%) [t(22)=5.0,
p<0.001] [main effect of task condition, F(2,21)=42.2, p<
0.001]. These results suggest that the NoGo task condition was
not sufficiently difficult to yield group differences in response
inhibition, or that 8- and 9-year-old children may have more
“mature” response inhibition abilities than interference control
skills (Bunge et al., 2002; van den Wildenberg and van der Molen,
2004; Liston et al., 2006; Bunge and Crone, 2009). The task
design may also have affected performance outcomes. In most
Go/NoGo paradigms, participants press a button on the go tri-
als and must override this prepotent response when a NoGo trial
appears. However, in this modified task presented herein, partic-
ipants had to analyze each stimulus array to determine whether
they should press a left button, a right button, or withhold their
response. Thus, children in this study were unlikely to have devel-
oped a prepotent response tendency because they were unable to
plan a motor response until the stimulus appeared. This may have
led to the high performance across all children on the NoGo tri-
als. Given these limitations, the present study focuses on results
regarding incongruent and neutral flanker task conditions that
required different amounts of cognitive control.
Improvements in task performance after 9 months were found
for all children, which were predicted with development and
practice. Shorter post-test RT across incongruent and neutral
trials (M=797.1 ms, SD =155.9ms)wasfound relative to pre-
test RT (M=883.4 ms, SD =135.2 ms) [main effect of time,
F(1,21)=18.8, p<0.001], with larger changes from pre-test to
post-test for incongruent RT [M=109.1 ms, SD =101.6 ms;
effect size (ES)=3.18] compared to neutral RT (M=66.7 ms,
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Chaddock-Heyman et al. Physical activity and fMRI activation
SD =92.5 ms; ES =2.27) [condition ×time interaction, F(1,21)=
8.2, p=0.009]. The main effect of time was only marginally signif-
icant for accuracy [F(1,42)=2.9, p=0.1], which suggested only
modest increases in task accuracy for all children from pre-test
(M=91.3%, SE =7.2%) to post-test (M=93.8%, SE =0.8%).
Physical activity and wait-list control groups
The group ×condition ×time interaction did not reach signif-
icance for RT [F(1,21)=0.17, p=0.68] or accuracy [F(1,42)=
0.29, p=0.59]. Change scores (difference between post-test per-
formance and pre-test performance) were also calculated for the
physical activity intervention group (PA) and wait-list control
group (C) (Incongruent RT—PA: M=117.8 ms, SD =71.4 ms,
C: M=95.5 ms, SD =140.5 ms; Neutral RT—PA: M=71.0 ms,
SD =80 ms, C: 60.4 ms, SE =113.3 ms; Incongruent accuracy—
PA: M=5.3%, SD =10.6%, C: M=3.1%, SD =16.7%; Neutral
accuracy—PA: M=4.3%, SD =5.8%, C: M=2.0%, SD =
9.4%; NoGo accuracy—PA: M=0.1%; SD =2.3%, C: M=
0.0%, SD =1.8%), but independent t-tests did not yield signif-
icant differences between the groups (all t<0.7, p>0.4).
Because of apriorihypotheses about greater changes in task
performance for the physical activity intervention group relative
to the wait-list control group, paired t-tests were conducted to
further explore the data. Consistent with hypotheses, the physical
activity intervention group showed shorter RT for both incon-
gruent trials [t(13)=6.2, p<0.001] and neutral trials [t(13)=
3.3, p=0.006] at post-test relative to pre-test (see Ta b l e 2 and
Figure 3A). The physical activity intervention group also showed
a trend for increased accuracy for incongruent trials [t(13)=1.9,
p=0.08] (see Figure 3B) and a significant increase in accuracy
for neutral trials [t(13)=2.5, p=0.03] at post-test relative to pre-
test, but no changes in accuracy for NoGo trials [t(13)=0.3, 0.8]
(see Ta b l e 2 ). Alternatively, the wait-list control group showed
a trend for shorter RT from pre-test to post-test [incongruent:
t(8)=2.1, p=0.08; neutral: t(8)=1.6, p=0.15], but no signif-
icant changes in accuracy from pre-test to post-test (all t<0.7,
all p>0.6) (see Ta b l e 2 ). The data raise the possibility that the
physical activity intervention group was responsible for some of
the general performance improvements across all children from
pre-test to post-test.
Children and young adults
To gain more insight into changes in task performance within the
physical activity intervention group and wait-list control group,
the pre-test and post-test task performance for the groups of chil-
dren were compared to a group of young adults (see Ta b l e 2 ). It
was predicted that children and young adults would differ in task
performance at pre-test, due to age effects, but participation in
physical activity may reduce the age effects at post-test.
As predicted, at pre-test, the physical activity intervention
group and wait-list control group showed longer RT than young
adults during incongruent [PA: t(36)=7.3, p<0.001; C: t(31)=
9.1, p<0.001] and neutral [PA: t(36)=7.7, p<0.001; C: t(31)=
5.6, p<0.001] trials. Both groups of children also showed lower
accuracy rates during incongruent [PA: t(36)=2.8, p=0.03;
C: t(31)=2.6, p=0.01] and neutral [PA: t(36)=3.6, p=0.01;
C: t(31)=2.8, p=0.008] trials.
However, at post-test, the physical activity intervention group
did not differ from young adults in terms of incongruent accu-
racy [t(36)=0.7, p=0.5] (see Figure 3B) or neutral accuracy
[t(36)=0.9, p=0.4]. Alternatively, the wait-list control group
still showed lower accuracy rates than young adults during
incongruent [t(31)=2.2, p=0.04] and neutral [t(31)=4.1, p<
0.001] task trials. In terms of RT, both the physical activity inter-
vention and wait-list control groups showed longer RT than
youngadultsduringincongruent[PA:t(36)=4.7, p<0.001; C:
t(31)=9.2, p<0.001] (see Figure 3A) and neutral [PA: t(36)=
5.2, p<0.001; C: t(31)=6.2, p<0.001] trials at post-test. No
age-related performance differences between the physical activ-
ity intervention group, wait-list control group and young adults
were found for NoGo trials at pre-test [PA: t(36)=0.2, p=0.9;
C: t(31)=0.3, p=0.7] or post-test [PA: t(36)=0.1, p=0.9; C:
t(31)=0.3, p=0.7].
In summary, the performance comparisons by age suggest that
all children showed significantly slower response speed and lower
accuracy rates during incongruent and neutral trials than young
adults at pre-test. Nine months later at post-test, all children still
performed the task more slowly than the young adults, but chil-
dren who participated in the physical activity intervention did not
differ from the adult group in terms of task accuracy. On the other
hand, the wait-list control group remained less accurate than the
young adults at post-test.
FUNCTIONAL ROIs
Ta b l e 3 contains mean percent signal change values of each ROI
(see Figure 2) at pre-test and post-test in the physical activity
intervention and wait-list control groups of children. Ta b l e 3 also
contains mean percent signal change values in each ROI for the
young adults.
Right anterior prefrontal cortex
Consistent with predictions, a significant group ×time interac-
tion [F(1,21)=5.4, p=0.03] demonstrated that children in the
physical activity intervention group and wait-list control group
showed differential changes in fMRI activation in the right ante-
rior prefrontal cortex, from pre-test to post-test. The physical
activity intervention group showed a significant decrease in right
anterior prefrontal activation across all task conditions from pre-
test (M=0.49, SD =0.34) to post-test (M=0.25, SD =0.41).
No change in activation from pre-test (M=0.47, SD =0.33)
to post-test (M=0.58, SD =0.45) was found for the wait-
list control group in the right anterior prefrontal cortex. In an
exploratory planned comparison, this change in activation that
was found for the physical activity intervention group was driven
by activation decreases during the incongruent condition [t(13)=
3.5, p=0.004] (see Figure 4A), and no significant within-group
changes in neutral or NoGo activation (see Ta b l e 3 ).
Similar to the performance comparisons above, pre-test and
post-test brain activation in the right anterior prefrontal cortex
of the physical activity intervention group and wait-list con-
trol group of children were compared to the activation of the
young adults in this ROI. Consistent with predictions, during
incongruent trials, which necessitated increased cognitive con-
trol, both the physical activity intervention group [t(36)=2.6,
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Chaddock-Heyman et al. Physical activity and fMRI activation
FIGURE 3 | (A) Change in incongruent RT for the physical activity
intervention group and wait-list control group. Error bars represent
standard error. The child groups showed longer RT at pre-test and
post-test compared to young adults. (B) Change in incongruent accuracy
for the physical activity intervention group and wait-list control group. The
within-group increase in accuracy for the physical activity intervention
group led to accuracy rates at post-test that did not differ from young
adults. Error bars represent standard error.
p=0.01] and the wait-list control group [t(31)=2.5, p=0.02]
had more activation in the right anterior prefrontal cortex com-
pared to the young adults at pre-test (see Ta b l e 3 ). At post-test,
activation of the right anterior prefrontal cortex in the physical
activity intervention group became statistically equivalent to the
young adults [t(36)=0.2, p=0.8] (see Figure 4A), whereas, the
wait-list control group still showed activation differences at post-
test [t(31)=2.9, p=0.008] (see Ta b l e 3 ). During neutral trials
(and NoGo trials), which required less cognitive control than
incongruent trials, neither group of children was statistically dif-
ferent in right anterior prefrontal cortex activation at pre-test or
post-test (all p>0.05).Insum,childreninthephysicalactivity
intervention group showed significant decreases in activation in
the right anterior prefrontal cortex from pre-test to post-test dur-
ing incongruent flanker trials, which led to activation patterns
that mirrored the patterns of young adults at post-test. Wait-list
control children did not show changes in right anterior prefrontal
cortex activation from pre-test to post-test and showed significant
activation differences from young adults at pre-test and post-test.
It is noteworthy that all children showed adult-like activation
during task trials that required less cognitive control (e.g., neu-
tral and NoGo trials). Furthermore, the data suggest that the
wait-list control children were unable to upregulate these pro-
cesses to support task conditions requiring additional control
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Chaddock-Heyman et al. Physical activity and fMRI activation
Table 3 | Mean percent signal change (SD) (range) for the physical activity (PA) and control (C) child groups and the young adult group during
incongruent, neutral and NoGo task trials (versus baseline).
PA pre-test PA post-test C pre-test C post-test Young
INCONGRUENT
Right anterior
prefrontal cortex
0.62 (0.5) (−0.01 to 1.31)a0.19 (0.6) (−1.07 to 1.40)a0.67 (0.5) (−0.13 to 1.71) 0.70 (0.4) (0.00 to 1.26) 0.22 (0.4) (−0.69 to 1.21)
Anterior cingulate
cortex
0.56 (0.5) (−0.21 to 1.35) 0.37 (0.4) (−0.53 to 2.12) 0.54 (0.4) (−0.02 to 0.76) 0.38 (0.3) (0.01 to 0.98) 0.38 (0.3) (−0.66 to 1.67)
NEUTRAL
Right anterior
prefrontal cortex
0.53 (0.5) (−0.21 to 1.35) 0.45 (0.7) (−0.35 to 1.70) 0.38 (0.3) (−0.02 to 0.76) 0.51 (0.3) (0.01 to 0.98) 0.23 (0.5) (−0.66 to 1.67)
Anterior cingulate
cortex
0.75 (0.5) (0.19 to 1.81) 0.52 (0.3) (0.02 to 1.22) 0.44 (0.4) (−0.11 to 0.89) 0.44 (0.3) (−0.28 to 0.89) 0.31 (0.3) (−0.21 to 0.98)
NOGO
Right anterior
prefrontal cortex
0.32 (0.6) (−0.35 to 1.70) 0.11 (0.6) (–1.09 to 1.07) 0.38 (0.2) (0.10 to 0.77) 0.51 (0.1) (0.28 to 0.80) 0.26 (0.4) (–0.59 to 0.93)
Anterior cingulate
cortex
0.30 (0.5) (−0.45 to 1.67) 0.27 (0.3) (−0.43 to 0.57) 0.18 (0.4) (−0.56 to 0.57) 0.11 (0.3) (−0.35 to 0.46) 0.17 (0.3) (−0.41 to 0.85)
Note: Values that share a common superscript are significantly different at p <0.05. Effect sizes (Cohen’s d) for the right anterior prefrontal cortex: PA pre-test to
post-test changes for the right anterior prefront al cortex: incongruent-ES =0.78, neutral-ES =0.13, NoGo-ES =0.35. C pre-test to post-test changes for the right
anterior prefrontal cortex: incongruent-ES =0.07 , neutral-ES =0.43, NoGo-ES =0.82.
(e.g., incongruent trials). This framework supports previous
fMRI and ERP studies in children (Pontifex et al., 2011; Chaddock
et al., 2012a), which showed, in cross-sectional studies, that
higher fit children and lower fit children had similar brain pat-
terns and performance during trials with low cognitive demands,
but only higher fit children were able to maintain performance
and adapt neural recruitment to successfully perform on trials
with increased cognitive demands.
Anterior cingulate cortex
In the ACC, the group ×time interaction was not sig-
nificant [F(1,21)=0.2, p=0.6] (see Ta b l e 3 and Figure 4B).
Furthermore, neither the physical activity intervention group nor
wait-list control group showed significant differences in activa-
tion from the young adult group at pre-test or post-test {all
t<1.8, p>0.07, except one significant difference for ACC acti-
vation during neutral trials between the physical activity group
and young adults only at pre-test [t(36)=3.3, p=0.002]}. These
findings suggest that brain function in the ACC did not signif-
icantly change from pre-test to post-test in children and that
children activated the ACC at a similar level to the young adults
at both pre-test and post-test.
Supplemental results
Two additional regions outside the hypothesized frontal cortex
were shown on the Zstatistic map of average activation during
all task conditions for all children, across pre-test and post-test
(insula and occipital pole) (see Figure 1). In an 8 mm peak ROI
in the insula (voxel coordinates of 59, 71, 40), the group ×
time interaction was not significant [F(1,21)=2.9, p=0.13].
Furthermore, neither the physical activity intervention group nor
wait-list control group showed significant differences in activa-
tion in the insula from the young adult group at pre-test or
post-test (all t<2.0, p>0.05). Additionally, in an 8 mm peak
ROI in the occipital pole (voxel coordinates of 33, 18, 39), the
group ×time interaction was not significant [F(1,21)=0.16,
p=0.69]. Furthermore, neither the physical activity intervention
group nor wait-list control group showed significant differences
in activation in the occipital pole from the young adult group at
pre-test or post-test (all t<1.6, p>0.10). These findings sug-
gest that brain function in the insula and occipital cortex did not
significantly change from pre-test to post-test in children and that
children activated these areas at a similar level to the young adults
at both pre-test and post-test.
DISCUSSION
This study had three main goals. First, this study examined the
areas of the brain, especially the prefrontal cortex, associated
with a task of cognitive control in children. Research has shown
that the frontal cortex is especially involved in cognitive control
(Cabeza and Nyberg, 2000; Bunge and Crone, 2009) and develop-
ment (Gogtay et al., 2004). Second, this study examined whether
a 9-month physical activity intervention would influence perfor-
mance on a task of cognitive control as well as the frontal brain
regions involved in processing challenging task demands, relative
to a wait-list control group. Third, this study explored whether
changes in performance and activation in the physical activity
intervention group and wait-list control group from pre-test to
post-test mirrored performance and activation in college-aged
young adults. Although this study was limited by a relatively
small sample size, the results extend investigations of how phys-
ical activity and individual differences in aerobic fitness might
be associated with improved brain function (via fMRI, ERP)
involved in cognitive control in children (Hillman et al., 2005,
2009; Davis et al., 2011; Pontifex et al., 2011; Voss et al., 2011;
Chaddock et al., 2012a). The findings provide a foundation for
future research to examine, with larger sample sizes, the effect of
physical activity on frontal brain function.
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Chaddock-Heyman et al. Physical activity and fMRI activation
FIGURE 4 | (A) Change in mean percent signal change in the right
anterior prefrontal cortex during incongruent flanker trials for the
physical activity intervention group and wait-list control group. Error bars
represent standard error. The decrease in activation for the physical
activity intervention group led to mean percent signal change at
post-test that did not differ from young adults. (B) Change in mean
percent signal change in the anterior cingulate cortex during incongruent
flanker trials for the physical activity intervention group and wait-list
control group. Error bars represent standard error. No significant group
differences were observed.
Regarding the first goal, two areas of the frontal cortex
were found to be associated with the cognitive control task
(Z-stat >6), independent of task condition or group assignment.
The task-related frontal regions were found in the right anterior
prefrontal cortex and the ACC. Both the anterior prefrontal cortex
and ACC are known to work together to comprise cognitive con-
trol networks (Dosenbach et al., 2007, 2008; Fair et al., 2007). The
anterior prefrontal cortex is involved in the maintenance of task
context, task goals, and cognitive control over time (i.e., across
the trials of a task) (Koechlin et al., 1999; Rushworth et al., 2004;
Dosenbach et al., 2006, 2007). For example, Koechlin et al. (1999)
demonstrated that the bilateral anterior frontal lobe (i.e., frontal
pole) was activated during a task that required participants to
keep in mind a main goal while processing and exploring con-
current subgoals. Because such goal maintenance skills are useful
for planning and reasoning (Koechlin et al., 1999), it is important
to understand how factors such as physical activity may influ-
ence brain function of this region during development, a critical
period in which the brain matures, learns, and forms connections
(Amso and Casey, 2006). The ACC is also known to play a role in
cognitive control, via the monitoring of response conflict (often
engendered through error production) and signaling the frontal
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Chaddock-Heyman et al. Physical activity and fMRI activation
cortex to regulate top-down cognitive control (Botvinick et al.,
2001; Dosenbach et al., 2007, 2008). Both of these areas have been
found to relate to physical activity and aerobic fitness across the
lifespan (Colcombe et al., 2004; Voss et al., 2011; Chaddock et al.,
2012a). The present study used a randomized controlled interven-
tion design in children to explore the effects of physicalactivity on
the fMRI brain function of both of these regions.
In regards to the second goal, a significant group ×time
interaction demonstrated that children in the physical activity
intervention group showed significant decreases in fMRI acti-
vation in the right anterior prefrontal cortex from pre-test to
post-test, whereas the activation patterns in this frontal region in
the wait-list control group remained unchanged. It is noteworthy
that exploratory planned comparisons revealed that these acti-
vation changes in the physical activity intervention group were
driven by decreases in activation during incongruent flanker tri-
als that required the greatest challenge to the participants’ ability
to pay attention and suppress distraction. In fact, relevant to the
third goal, the activation decreases in the physical activity inter-
vention group during the incongruent flanker condition led to
post-test fMRI patterns in the right anterior prefrontal cortex
that did not differ in magnitude from young adult activation. On
the other hand, children in the wait-list control group differed
from young adults in right anterior prefrontal activation during
incongruent flanker trials at both pre-test to post-test.
Together, these group-related and age-related activation pat-
terns raise the possibility that participation in physical activity
during childhood can lead to more adult-like recruitment of
anterior prefrontal brain areas important for maintenance and
goal-oriented cognitive control. Here, improved brain function is
associated with decreases in anterior prefrontal cortex activation
from pre-test to post-test, which is consistent with the framework
that less brain activation reflects more mature brain function, as a
number of studies show decreased activation and superior perfor-
mance on cognitive tasks in adults compared to children (Casey
et al., 1997; Durston et al., 2002; Booth et al., 2003; Scherf et al.,
2006; Velanova et al., 2008). Behaviorally, exploratory planned
comparisons demonstrated that the physical activity interven-
tion group showed within-group performance improvements in
terms of both speed and accuracy during incongruent and neu-
tral flanker trials. The incongruent accuracy rates of the physical
activity intervention children at post-test also mirrored those
of the young adults. In contrast, wait-list control children did
not show changes in task performance from pre-test to post-
test. These performance differences could be driven by changes
in maintenance of task context and task goals with the physical
activity intervention, which are functions linked to the anterior
prefrontal cortex (Koechlin et al., 1999; Rushworth et al., 2004;
Dosenbach et al., 2006, 2007).
In fact, previous studies have demonstrated an association
between physical activity, aerobic fitness, and anterior prefrontal
brain function involved in goal maintenance across the lifespan
(Voss et al., 2010, 2011; Kamijo et al., 2011). This longitudinal
intervention study in children extends and strengthens these find-
ings. In children, a cross-sectional fMRI study by Vo ss et a l. (2011)
demonstrated that higher fit children showed less activation in a
network of brain regions including the anterior prefrontal cortex,
coupled with better flanker task performance, relative to lower fit
children. An ERP study by Kamijo et al. (2011)alsodemonstrated
that children involved in a physical activity intervention showed
larger amplitudes over the frontal scalp regions in the contingent
negative variation (CNV), an ERP component known to play a
role in cognitive preparation and task maintenance, as well as
better working memory performance. In older adults, a physi-
cal activity intervention that involved walking 3 days per week,
for 1 year, led to changes in functional connectivity in a frontal-
executive network (Voss et al., 2010), a network that includes
the right and left anterior prefrontal cortex (Dosenbach et al.,
2006). The results of the present study contribute to this litera-
ture and suggest plasticity of the right anterior prefrontal cortex
with prolonged physical activity participation.
No changes in activation for the physical activity intervention
group or wait-list control group were found in the ACC. In addi-
tion, no differences were observed in the comparison of child
and adult ACC activation at pre-test or post-test. Consistent with
these findings, a cross-sectional study of the association between
aerobic fitness and cognitive control in children did not demon-
strate fitness differences in the ACC during incongruent flanker
trials (Voss et al., 2011). Further, Chaddock et al. (2012a)also
reported few fitness-related activation differences in this area.
However, higher fit children (Pontifex et al., 2011), higher fit
younger adults (Themanson et al., 2008), and higher fit older
adults (Colcombe et al., 2004), as well as older adults involved in a
physical activity intervention (Colcombe et al., 2004), have shown
smaller ERN amplitudes [an ERP component said to originate in
the dorsal portion of the ACC (Dehaene et al., 1994; Carter et al.,
1998; van Veen and Carter, 2002; Miltner et al., 2003)], and less
ACC activation, respectively, which are associated with perfor-
mance improvements on a flanker task. Such activation patterns
in the ACC are usually interpreted as a reduction in conflict or a
lower threshold for the detection and signaling of conflict to the
prefrontal cortex, which leads to better error detection. To address
this divergent evidence, additional research is needed to better
understand different responses to physical activity in children and
older adults, how effects in extreme fitness groups (higher fit,
lower fit) in cross-sectional studies differ from effects of an inter-
vention with lower fit individuals, as well as how ERP components
map onto fMRI activity.
The data also raise the possibility that the two groups of chil-
dren differed in their cognitive strategies at post-test. Cognitive
control strategies are theorized to develop from one that is more
rapid and reactive (i.e., reactive control) to one that can flexi-
bly sustain goal-oriented control (i.e., proactive control) (Braver
et al., 2007, 2009; Fair et al., 2007). Participation in physical
activity during childhood may influence fMRI brain patterns
underlying control strategies, specifically the anterior prefrontal
cortex (Fair et al., 2007; Paxton et al., 2008). That is, physically
active children may learn to maintain a sustained task set during
cognitive demands that require selective attention and distraction
suppression, which may lead to a more proactive control strategy
as well as more accurate and adult-like task performance. This
would parallel research that suggests that higher fit children and
older adults use a more proactive control neural strategy than
lower fit individuals, especially during incongruent flanker task
Frontiers in Human Neuroscience www.frontiersin.org March2013|Volume7|Article72|11
Chaddock-Heyman et al. Physical activity and fMRI activation
conditions (Colcombe et al., 2004; Pontifex et al., 2011; Voss et al.,
2011). Alternatively, children in a wait-list control group may be
less able to adapt their task strategy and task set at post-test, and
may continue to use a more reactive strategy, given that anterior
prefrontal activation and performance on incongruent task trials
were unchanged.
These results have important implications for public health
and the educational environment. Physical activity opportuni-
ties are being reduced or eliminated during the school day as
well as decreasing outside the school environment (Tro ian o et al. ,
2008). Children are becoming increasingly sedentary and unfit,
which leads to an increased risk for disease and obesity (United
States Department of Health and Human Services, 2008; Centers
for Disease Control and Prevention, 2009), as well as cognitive
impairment (Chaddock et al., 2012c). The present study suggests
that physical activity is important to the development of the brain
and cognition during childhood. These results should raise public
awareness of the cognitive benefits of being active and encourage
participation in a multicomponent physical activity program such
as physical education, classroom activity breaks, and active trans-
port to school (United States Department of Health and Human
Services, 2013).
ACKNOWLEDGMENTS
The National Institute of Child Health and Human Development
(NICHD) provided the principal source of funding (RO1
HD055352 and RO1 HD069381). Thank you to Holly Tracy,
Nancy Dodge, and John Powers for their help with data collection.
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Conflict of Interest Statement: The
authors declare that the research
was conducted in the absence of any
commercial or financial relationships
that could be construed as a potential
conflict of interest.
Received: 16 January 2013; accepted:
25 February 2013; published online: 12
March 2013.
Citation: Chaddock-Heyman L, Erickson
KI, Voss MW, Knecht AM, Pontifex
MB, Castelli DM, Hillman CH and
Kramer AF (2013) The effects of phys-
ical activity on functional MRI activa-
tion associated with cognitive control in
children: a randomized controlled inter-
vention. Front. Hum. Neurosci. 7:72. doi:
10.3389/fnhum.2013.00072
Copyright © 2013 Chaddock-Heyman,
Erickson, Voss, Knecht, Pontifex, Castelli,
Hillman and Kramer. This is an open-
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and reproduction in other forums, pro-
vided the original authors and source
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